Overview

Dataset statistics

Number of variables42
Number of observations768
Missing cells1079
Missing cells (%)3.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory649.5 KiB
Average record size in memory866.0 B

Variable types

Categorical18
DateTime2
Numeric22

Dataset

DescriptionQuality-verified clinical data for JHB_DPHRU_013
CreatorHEAT Research Programme
AuthorRP2 Clinical Data Team
URLhttps://github.com/Logic06183/RP2_dataoverview

Alerts

study_source has constant value "JHB_DPHRU_013"Constant
latitude has constant value "-26.2041"Constant
longitude has constant value "28.0473"Constant
jhb_subregion has constant value "Central_JHB"Constant
city has constant value "Johannesburg"Constant
province has constant value "Gauteng"Constant
country has constant value "South Africa"Constant
study_site_location has constant value "Central Johannesburg (DPHRU)"Constant
total_protein_extreme_flag has constant value "0.0"Constant
HEAT_VULNERABILITY_SCORE has constant value "0.0"Constant
HEAT_STRESS_RISK_CATEGORY has constant value "LOW"Constant
climate_heat_day_p95 has constant value "0.0"Constant
climate_p90_threshold has constant value "28.409"Constant
climate_p95_threshold has constant value "29.704"Constant
climate_p99_threshold has constant value "31.797"Constant
BMI (kg/m²) is highly overall correlated with Waist circumference (cm) and 1 other fieldsHigh correlation
FASTING HDL is highly overall correlated with climate_heat_day_p90 and 2 other fieldsHigh correlation
FASTING LDL is highly overall correlated with climate_heat_day_p90 and 2 other fieldsHigh correlation
Waist circumference (cm) is highly overall correlated with BMI (kg/m²) and 2 other fieldsHigh correlation
climate_14d_mean_temp is highly overall correlated with climate_30d_mean_temp and 6 other fieldsHigh correlation
climate_30d_mean_temp is highly overall correlated with climate_14d_mean_temp and 6 other fieldsHigh correlation
climate_7d_max_temp is highly overall correlated with climate_30d_mean_temp and 5 other fieldsHigh correlation
climate_7d_mean_temp is highly overall correlated with climate_14d_mean_temp and 7 other fieldsHigh correlation
climate_daily_max_temp is highly overall correlated with climate_daily_mean_temp and 6 other fieldsHigh correlation
climate_daily_mean_temp is highly overall correlated with climate_14d_mean_temp and 8 other fieldsHigh correlation
climate_daily_min_temp is highly overall correlated with climate_14d_mean_temp and 7 other fieldsHigh correlation
climate_heat_day_p90 is highly overall correlated with FASTING HDL and 10 other fieldsHigh correlation
climate_heat_stress_index is highly overall correlated with climate_daily_max_temp and 2 other fieldsHigh correlation
climate_season is highly overall correlated with climate_14d_mean_temp and 10 other fieldsHigh correlation
climate_standardized_anomaly is highly overall correlated with climate_daily_max_temp and 3 other fieldsHigh correlation
climate_temp_anomaly is highly overall correlated with climate_14d_mean_temp and 8 other fieldsHigh correlation
hdl_cholesterol_mg_dL is highly overall correlated with FASTING HDL and 2 other fieldsHigh correlation
height_m is highly overall correlated with climate_heat_day_p90High correlation
ldl_cholesterol_mg_dL is highly overall correlated with FASTING LDL and 2 other fieldsHigh correlation
month is highly overall correlated with climate_7d_mean_temp and 3 other fieldsHigh correlation
total_cholesterol_mg_dL is highly overall correlated with FASTING HDL and 4 other fieldsHigh correlation
weight_kg is highly overall correlated with BMI (kg/m²) and 2 other fieldsHigh correlation
year is highly overall correlated with climate_14d_mean_temp and 7 other fieldsHigh correlation
climate_heat_day_p90 is highly imbalanced (98.6%)Imbalance
Age (at enrolment) has 14 (1.8%) missing valuesMissing
FASTING HDL has 58 (7.6%) missing valuesMissing
FASTING LDL has 58 (7.6%) missing valuesMissing
Waist circumference (cm) has 205 (26.7%) missing valuesMissing
fasting_glucose_mmol_L has 32 (4.2%) missing valuesMissing
total_cholesterol_mg_dL has 59 (7.7%) missing valuesMissing
hdl_cholesterol_mg_dL has 58 (7.6%) missing valuesMissing
ldl_cholesterol_mg_dL has 58 (7.6%) missing valuesMissing
weight_kg has 205 (26.7%) missing valuesMissing
height_m has 331 (43.1%) missing valuesMissing

Reproduction

Analysis started2025-11-25 05:34:18.780747
Analysis finished2025-11-25 05:34:36.463510
Duration17.68 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

study_source
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size52.5 KiB
JHB_DPHRU_013
768 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters9984
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJHB_DPHRU_013
2nd rowJHB_DPHRU_013
3rd rowJHB_DPHRU_013
4th rowJHB_DPHRU_013
5th rowJHB_DPHRU_013

Common Values

ValueCountFrequency (%)
JHB_DPHRU_013768
100.0%

Length

2025-11-25T07:34:36.486317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:34:36.519228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
jhb_dphru_013768
100.0%

Most occurring characters

ValueCountFrequency (%)
H1536
15.4%
_1536
15.4%
J768
7.7%
B768
7.7%
D768
7.7%
P768
7.7%
R768
7.7%
U768
7.7%
0768
7.7%
1768
7.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter6144
61.5%
Decimal Number2304
 
23.1%
Connector Punctuation1536
 
15.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
H1536
25.0%
J768
12.5%
B768
12.5%
D768
12.5%
P768
12.5%
R768
12.5%
U768
12.5%
Decimal Number
ValueCountFrequency (%)
0768
33.3%
1768
33.3%
3768
33.3%
Connector Punctuation
ValueCountFrequency (%)
_1536
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6144
61.5%
Common3840
38.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
H1536
25.0%
J768
12.5%
B768
12.5%
D768
12.5%
P768
12.5%
R768
12.5%
U768
12.5%
Common
ValueCountFrequency (%)
_1536
40.0%
0768
20.0%
1768
20.0%
3768
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII9984
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
H1536
15.4%
_1536
15.4%
J768
7.7%
B768
7.7%
D768
7.7%
P768
7.7%
R768
7.7%
U768
7.7%
0768
7.7%
1768
7.7%
Distinct232
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
Minimum2011-02-10 00:00:00
Maximum2013-06-19 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-25T07:34:36.558288image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:36.610841image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

year
Categorical

High correlation 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
2011.0
446 
2012.0
195 
2013.0
127 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters4608
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2011.0
2nd row2011.0
3rd row2012.0
4th row2012.0
5th row2013.0

Common Values

ValueCountFrequency (%)
2011.0446
58.1%
2012.0195
25.4%
2013.0127
 
16.5%

Length

2025-11-25T07:34:36.661620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:34:36.699955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2011.0446
58.1%
2012.0195
25.4%
2013.0127
 
16.5%

Most occurring characters

ValueCountFrequency (%)
01536
33.3%
11214
26.3%
2963
20.9%
.768
16.7%
3127
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3840
83.3%
Other Punctuation768
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01536
40.0%
11214
31.6%
2963
25.1%
3127
 
3.3%
Other Punctuation
ValueCountFrequency (%)
.768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4608
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01536
33.3%
11214
26.3%
2963
20.9%
.768
16.7%
3127
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII4608
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01536
33.3%
11214
26.3%
2963
20.9%
.768
16.7%
3127
 
2.8%

month
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.171875
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T07:34:36.736082image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q38
95-th percentile10
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.6502595
Coefficient of variation (CV)0.51243688
Kurtosis-0.49910842
Mean5.171875
Median Absolute Deviation (MAD)1
Skewness0.79268233
Sum3972
Variance7.0238755
MonotonicityNot monotonic
2025-11-25T07:34:36.773771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
4173
22.5%
5143
18.6%
3129
16.8%
292
12.0%
878
10.2%
1046
 
6.0%
940
 
5.2%
1134
 
4.4%
624
 
3.1%
16
 
0.8%
Other values (2)3
 
0.4%
ValueCountFrequency (%)
16
 
0.8%
292
12.0%
3129
16.8%
4173
22.5%
5143
18.6%
624
 
3.1%
72
 
0.3%
878
10.2%
940
 
5.2%
1046
 
6.0%
ValueCountFrequency (%)
121
 
0.1%
1134
 
4.4%
1046
 
6.0%
940
 
5.2%
878
10.2%
72
 
0.3%
624
 
3.1%
5143
18.6%
4173
22.5%
3129
16.8%

latitude
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size48.8 KiB
-26.2041
768 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters6144
Distinct characters7
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-26.2041
2nd row-26.2041
3rd row-26.2041
4th row-26.2041
5th row-26.2041

Common Values

ValueCountFrequency (%)
-26.2041768
100.0%

Length

2025-11-25T07:34:36.819404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:34:36.854349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
26.2041768
100.0%

Most occurring characters

ValueCountFrequency (%)
21536
25.0%
-768
12.5%
6768
12.5%
.768
12.5%
0768
12.5%
4768
12.5%
1768
12.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4608
75.0%
Dash Punctuation768
 
12.5%
Other Punctuation768
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
21536
33.3%
6768
16.7%
0768
16.7%
4768
16.7%
1768
16.7%
Dash Punctuation
ValueCountFrequency (%)
-768
100.0%
Other Punctuation
ValueCountFrequency (%)
.768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common6144
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
21536
25.0%
-768
12.5%
6768
12.5%
.768
12.5%
0768
12.5%
4768
12.5%
1768
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII6144
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21536
25.0%
-768
12.5%
6768
12.5%
.768
12.5%
0768
12.5%
4768
12.5%
1768
12.5%

longitude
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size48.0 KiB
28.0473
768 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters5376
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row28.0473
2nd row28.0473
3rd row28.0473
4th row28.0473
5th row28.0473

Common Values

ValueCountFrequency (%)
28.0473768
100.0%

Length

2025-11-25T07:34:36.890569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:34:36.924370image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
28.0473768
100.0%

Most occurring characters

ValueCountFrequency (%)
2768
14.3%
8768
14.3%
.768
14.3%
0768
14.3%
4768
14.3%
7768
14.3%
3768
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4608
85.7%
Other Punctuation768
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2768
16.7%
8768
16.7%
0768
16.7%
4768
16.7%
7768
16.7%
3768
16.7%
Other Punctuation
ValueCountFrequency (%)
.768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5376
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2768
14.3%
8768
14.3%
.768
14.3%
0768
14.3%
4768
14.3%
7768
14.3%
3768
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII5376
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2768
14.3%
8768
14.3%
.768
14.3%
0768
14.3%
4768
14.3%
7768
14.3%
3768
14.3%

jhb_subregion
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size51.0 KiB
Central_JHB
768 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters8448
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCentral_JHB
2nd rowCentral_JHB
3rd rowCentral_JHB
4th rowCentral_JHB
5th rowCentral_JHB

Common Values

ValueCountFrequency (%)
Central_JHB768
100.0%

Length

2025-11-25T07:34:36.958220image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:34:36.991274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
central_jhb768
100.0%

Most occurring characters

ValueCountFrequency (%)
C768
9.1%
e768
9.1%
n768
9.1%
t768
9.1%
r768
9.1%
a768
9.1%
l768
9.1%
_768
9.1%
J768
9.1%
H768
9.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4608
54.5%
Uppercase Letter3072
36.4%
Connector Punctuation768
 
9.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e768
16.7%
n768
16.7%
t768
16.7%
r768
16.7%
a768
16.7%
l768
16.7%
Uppercase Letter
ValueCountFrequency (%)
C768
25.0%
J768
25.0%
H768
25.0%
B768
25.0%
Connector Punctuation
ValueCountFrequency (%)
_768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7680
90.9%
Common768
 
9.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
C768
10.0%
e768
10.0%
n768
10.0%
t768
10.0%
r768
10.0%
a768
10.0%
l768
10.0%
J768
10.0%
H768
10.0%
B768
10.0%
Common
ValueCountFrequency (%)
_768
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII8448
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C768
9.1%
e768
9.1%
n768
9.1%
t768
9.1%
r768
9.1%
a768
9.1%
l768
9.1%
_768
9.1%
J768
9.1%
H768
9.1%

city
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size51.8 KiB
Johannesburg
768 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters9216
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJohannesburg
2nd rowJohannesburg
3rd rowJohannesburg
4th rowJohannesburg
5th rowJohannesburg

Common Values

ValueCountFrequency (%)
Johannesburg768
100.0%

Length

2025-11-25T07:34:37.025905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:34:37.059047image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
johannesburg768
100.0%

Most occurring characters

ValueCountFrequency (%)
n1536
16.7%
J768
8.3%
o768
8.3%
h768
8.3%
a768
8.3%
e768
8.3%
s768
8.3%
b768
8.3%
u768
8.3%
r768
8.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8448
91.7%
Uppercase Letter768
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n1536
18.2%
o768
9.1%
h768
9.1%
a768
9.1%
e768
9.1%
s768
9.1%
b768
9.1%
u768
9.1%
r768
9.1%
g768
9.1%
Uppercase Letter
ValueCountFrequency (%)
J768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin9216
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n1536
16.7%
J768
8.3%
o768
8.3%
h768
8.3%
a768
8.3%
e768
8.3%
s768
8.3%
b768
8.3%
u768
8.3%
r768
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII9216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n1536
16.7%
J768
8.3%
o768
8.3%
h768
8.3%
a768
8.3%
e768
8.3%
s768
8.3%
b768
8.3%
u768
8.3%
r768
8.3%

province
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size48.0 KiB
Gauteng
768 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters5376
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGauteng
2nd rowGauteng
3rd rowGauteng
4th rowGauteng
5th rowGauteng

Common Values

ValueCountFrequency (%)
Gauteng768
100.0%

Length

2025-11-25T07:34:37.095189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:34:37.129787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
gauteng768
100.0%

Most occurring characters

ValueCountFrequency (%)
G768
14.3%
a768
14.3%
u768
14.3%
t768
14.3%
e768
14.3%
n768
14.3%
g768
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4608
85.7%
Uppercase Letter768
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a768
16.7%
u768
16.7%
t768
16.7%
e768
16.7%
n768
16.7%
g768
16.7%
Uppercase Letter
ValueCountFrequency (%)
G768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5376
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G768
14.3%
a768
14.3%
u768
14.3%
t768
14.3%
e768
14.3%
n768
14.3%
g768
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII5376
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G768
14.3%
a768
14.3%
u768
14.3%
t768
14.3%
e768
14.3%
n768
14.3%
g768
14.3%

country
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size51.8 KiB
South Africa
768 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters9216
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouth Africa
2nd rowSouth Africa
3rd rowSouth Africa
4th rowSouth Africa
5th rowSouth Africa

Common Values

ValueCountFrequency (%)
South Africa768
100.0%

Length

2025-11-25T07:34:37.165090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:34:37.197787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
south768
50.0%
africa768
50.0%

Most occurring characters

ValueCountFrequency (%)
S768
8.3%
o768
8.3%
u768
8.3%
t768
8.3%
h768
8.3%
768
8.3%
A768
8.3%
f768
8.3%
r768
8.3%
i768
8.3%
Other values (2)1536
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6912
75.0%
Uppercase Letter1536
 
16.7%
Space Separator768
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o768
11.1%
u768
11.1%
t768
11.1%
h768
11.1%
f768
11.1%
r768
11.1%
i768
11.1%
c768
11.1%
a768
11.1%
Uppercase Letter
ValueCountFrequency (%)
S768
50.0%
A768
50.0%
Space Separator
ValueCountFrequency (%)
768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8448
91.7%
Common768
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
S768
9.1%
o768
9.1%
u768
9.1%
t768
9.1%
h768
9.1%
A768
9.1%
f768
9.1%
r768
9.1%
i768
9.1%
c768
9.1%
Common
ValueCountFrequency (%)
768
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII9216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S768
8.3%
o768
8.3%
u768
8.3%
t768
8.3%
h768
8.3%
768
8.3%
A768
8.3%
f768
8.3%
r768
8.3%
i768
8.3%
Other values (2)1536
16.7%

Age (at enrolment)
Real number (ℝ)

Missing 

Distinct183
Distinct (%)24.3%
Missing14
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean33.533554
Minimum18.1
Maximum51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T07:34:37.235858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum18.1
5-th percentile22
Q127.85
median33.95
Q339
95-th percentile46
Maximum51
Range32.9
Interquartile range (IQR)11.15

Descriptive statistics

Standard deviation7.3527855
Coefficient of variation (CV)0.21926651
Kurtosis-0.80768914
Mean33.533554
Median Absolute Deviation (MAD)5.95
Skewness0.055500259
Sum25284.3
Variance54.063454
MonotonicityNot monotonic
2025-11-25T07:34:37.284698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4032
 
4.2%
3431
 
4.0%
3931
 
4.0%
3729
 
3.8%
3027
 
3.5%
3125
 
3.3%
3823
 
3.0%
4122
 
2.9%
2621
 
2.7%
3521
 
2.7%
Other values (173)492
64.1%
ValueCountFrequency (%)
18.11
 
0.1%
18.81
 
0.1%
193
 
0.4%
19.31
 
0.1%
19.42
 
0.3%
19.51
 
0.1%
19.61
 
0.1%
209
1.2%
20.11
 
0.1%
20.61
 
0.1%
ValueCountFrequency (%)
511
 
0.1%
503
 
0.4%
49.11
 
0.1%
495
0.7%
488
1.0%
47.91
 
0.1%
47.21
 
0.1%
4710
1.3%
46.61
 
0.1%
46.41
 
0.1%

date
Date

Distinct232
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
Minimum2011-02-10 00:00:00
Maximum2013-06-19 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-25T07:34:37.333638image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:37.385673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

study_site_location
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size63.8 KiB
Central Johannesburg (DPHRU)
768 

Length

Max length28
Median length28
Mean length28
Min length28

Characters and Unicode

Total characters21504
Distinct characters22
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCentral Johannesburg (DPHRU)
2nd rowCentral Johannesburg (DPHRU)
3rd rowCentral Johannesburg (DPHRU)
4th rowCentral Johannesburg (DPHRU)
5th rowCentral Johannesburg (DPHRU)

Common Values

ValueCountFrequency (%)
Central Johannesburg (DPHRU)768
100.0%

Length

2025-11-25T07:34:37.434849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:34:37.470008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
central768
33.3%
johannesburg768
33.3%
dphru768
33.3%

Most occurring characters

ValueCountFrequency (%)
n2304
 
10.7%
r1536
 
7.1%
a1536
 
7.1%
1536
 
7.1%
e1536
 
7.1%
C768
 
3.6%
g768
 
3.6%
U768
 
3.6%
R768
 
3.6%
H768
 
3.6%
Other values (12)9216
42.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter13056
60.7%
Uppercase Letter5376
25.0%
Space Separator1536
 
7.1%
Open Punctuation768
 
3.6%
Close Punctuation768
 
3.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n2304
17.6%
r1536
11.8%
a1536
11.8%
e1536
11.8%
g768
 
5.9%
s768
 
5.9%
u768
 
5.9%
b768
 
5.9%
h768
 
5.9%
o768
 
5.9%
Other values (2)1536
11.8%
Uppercase Letter
ValueCountFrequency (%)
C768
14.3%
U768
14.3%
R768
14.3%
H768
14.3%
P768
14.3%
D768
14.3%
J768
14.3%
Space Separator
ValueCountFrequency (%)
1536
100.0%
Open Punctuation
ValueCountFrequency (%)
(768
100.0%
Close Punctuation
ValueCountFrequency (%)
)768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin18432
85.7%
Common3072
 
14.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
n2304
 
12.5%
r1536
 
8.3%
a1536
 
8.3%
e1536
 
8.3%
C768
 
4.2%
g768
 
4.2%
U768
 
4.2%
R768
 
4.2%
H768
 
4.2%
P768
 
4.2%
Other values (9)6912
37.5%
Common
ValueCountFrequency (%)
1536
50.0%
(768
25.0%
)768
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII21504
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n2304
 
10.7%
r1536
 
7.1%
a1536
 
7.1%
1536
 
7.1%
e1536
 
7.1%
C768
 
3.6%
g768
 
3.6%
U768
 
3.6%
R768
 
3.6%
H768
 
3.6%
Other values (12)9216
42.9%

FASTING HDL
Real number (ℝ)

High correlation  Missing 

Distinct174
Distinct (%)24.5%
Missing58
Missing (%)7.6%
Infinite0
Infinite (%)0.0%
Mean1.1211127
Minimum0.28
Maximum3.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T07:34:37.507309image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.28
5-th percentile0.51
Q10.83
median1.07
Q31.37
95-th percentile1.8855
Maximum3.7
Range3.42
Interquartile range (IQR)0.54

Descriptive statistics

Standard deviation0.44352229
Coefficient of variation (CV)0.39560902
Kurtosis4.4712255
Mean1.1211127
Median Absolute Deviation (MAD)0.26
Skewness1.2913394
Sum795.99
Variance0.19671202
MonotonicityNot monotonic
2025-11-25T07:34:37.555036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.813
 
1.7%
1.0413
 
1.7%
0.8513
 
1.7%
0.9313
 
1.7%
1.113
 
1.7%
1.1811
 
1.4%
0.9511
 
1.4%
110
 
1.3%
0.8410
 
1.3%
0.879
 
1.2%
Other values (164)594
77.3%
(Missing)58
 
7.6%
ValueCountFrequency (%)
0.281
0.1%
0.321
0.1%
0.332
0.3%
0.342
0.3%
0.351
0.1%
0.362
0.3%
0.372
0.3%
0.391
0.1%
0.42
0.3%
0.412
0.3%
ValueCountFrequency (%)
3.73
0.4%
2.81
 
0.1%
2.532
0.3%
2.491
 
0.1%
2.441
 
0.1%
2.311
 
0.1%
2.31
 
0.1%
2.291
 
0.1%
2.242
0.3%
2.231
 
0.1%

FASTING LDL
Real number (ℝ)

High correlation  Missing 

Distinct261
Distinct (%)36.8%
Missing58
Missing (%)7.6%
Infinite0
Infinite (%)0.0%
Mean1.6717042
Minimum0
Maximum6.04
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T07:34:37.602695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.6745
Q11.11
median1.535
Q32.07
95-th percentile3.18
Maximum6.04
Range6.04
Interquartile range (IQR)0.96

Descriptive statistics

Standard deviation0.77008108
Coefficient of variation (CV)0.4606563
Kurtosis1.8978142
Mean1.6717042
Median Absolute Deviation (MAD)0.475
Skewness1.0866871
Sum1186.91
Variance0.59302488
MonotonicityNot monotonic
2025-11-25T07:34:37.651056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.019
 
1.2%
1.129
 
1.2%
1.329
 
1.2%
1.378
 
1.0%
1.298
 
1.0%
1.187
 
0.9%
2.067
 
0.9%
1.947
 
0.9%
1.267
 
0.9%
1.767
 
0.9%
Other values (251)632
82.3%
(Missing)58
 
7.6%
ValueCountFrequency (%)
01
 
0.1%
0.331
 
0.1%
0.391
 
0.1%
0.422
 
0.3%
0.451
 
0.1%
0.461
 
0.1%
0.471
 
0.1%
0.53
0.4%
0.555
0.7%
0.564
0.5%
ValueCountFrequency (%)
6.041
0.1%
4.411
0.1%
4.281
0.1%
4.252
0.3%
4.191
0.1%
4.131
0.1%
3.971
0.1%
3.941
0.1%
3.891
0.1%
3.871
0.1%

BMI (kg/m²)
Real number (ℝ)

High correlation 

Distinct248
Distinct (%)32.3%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean27.852803
Minimum15.1
Maximum57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T07:34:37.697595image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum15.1
5-th percentile19.13
Q123
median26.7
Q331.5
95-th percentile40.54
Maximum57
Range41.9
Interquartile range (IQR)8.5

Descriptive statistics

Standard deviation6.6900116
Coefficient of variation (CV)0.24019168
Kurtosis1.6551874
Mean27.852803
Median Absolute Deviation (MAD)4.1
Skewness1.0682353
Sum21363.1
Variance44.756255
MonotonicityNot monotonic
2025-11-25T07:34:37.742256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.311
 
1.4%
2510
 
1.3%
21.810
 
1.3%
26.79
 
1.2%
27.48
 
1.0%
25.88
 
1.0%
32.38
 
1.0%
21.58
 
1.0%
22.98
 
1.0%
28.97
 
0.9%
Other values (238)680
88.5%
ValueCountFrequency (%)
15.11
0.1%
15.31
0.1%
161
0.1%
16.11
0.1%
16.61
0.1%
16.81
0.1%
16.91
0.1%
17.11
0.1%
17.21
0.1%
17.31
0.1%
ValueCountFrequency (%)
571
 
0.1%
56.11
 
0.1%
54.91
 
0.1%
54.31
 
0.1%
50.71
 
0.1%
50.41
 
0.1%
50.11
 
0.1%
49.83
0.4%
491
 
0.1%
46.42
0.3%

Waist circumference (cm)
Real number (ℝ)

High correlation  Missing 

Distinct115
Distinct (%)20.4%
Missing205
Missing (%)26.7%
Infinite0
Infinite (%)0.0%
Mean89.362345
Minimum2.9
Maximum915
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T07:34:37.873630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.9
5-th percentile67.55
Q178
median86.5
Q396.5
95-th percentile115.25
Maximum915
Range912.1
Interquartile range (IQR)18.5

Descriptive statistics

Standard deviation38.256862
Coefficient of variation (CV)0.42810942
Kurtosis387.32681
Mean89.362345
Median Absolute Deviation (MAD)8.5
Skewness17.935657
Sum50311
Variance1463.5875
MonotonicityNot monotonic
2025-11-25T07:34:37.919956image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8722
 
2.9%
8519
 
2.5%
8119
 
2.5%
7818
 
2.3%
8918
 
2.3%
8617
 
2.2%
7916
 
2.1%
7416
 
2.1%
7615
 
2.0%
7713
 
1.7%
Other values (105)390
50.8%
(Missing)205
26.7%
ValueCountFrequency (%)
2.91
 
0.1%
8.11
 
0.1%
10.81
 
0.1%
591
 
0.1%
612
0.3%
621
 
0.1%
634
0.5%
641
 
0.1%
652
0.3%
664
0.5%
ValueCountFrequency (%)
9151
0.1%
1511
0.1%
1451
0.1%
143.51
0.1%
1401
0.1%
1331
0.1%
1311
0.1%
1301
0.1%
129.51
0.1%
1282
0.3%

fasting_glucose_mmol_L
Real number (ℝ)

Missing 

Distinct276
Distinct (%)37.5%
Missing32
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean4.928356
Minimum0.95
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T07:34:37.964388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.95
5-th percentile3.35
Q14.5
median4.93
Q35.4125
95-th percentile6.12
Maximum15
Range14.05
Interquartile range (IQR)0.9125

Descriptive statistics

Standard deviation0.95305831
Coefficient of variation (CV)0.1933826
Kurtosis19.566982
Mean4.928356
Median Absolute Deviation (MAD)0.45
Skewness1.5235001
Sum3627.27
Variance0.90832015
MonotonicityNot monotonic
2025-11-25T07:34:38.011363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.219
 
1.2%
4.759
 
1.2%
5.428
 
1.0%
4.828
 
1.0%
4.938
 
1.0%
4.78
 
1.0%
5.248
 
1.0%
4.737
 
0.9%
4.577
 
0.9%
5.177
 
0.9%
Other values (266)657
85.5%
(Missing)32
 
4.2%
ValueCountFrequency (%)
0.951
0.1%
1.121
0.1%
1.371
0.1%
1.471
0.1%
2.021
0.1%
2.041
0.1%
2.211
0.1%
2.221
0.1%
2.261
0.1%
2.552
0.3%
ValueCountFrequency (%)
151
0.1%
9.911
0.1%
9.671
0.1%
8.241
0.1%
7.971
0.1%
7.621
0.1%
7.611
0.1%
7.421
0.1%
7.271
0.1%
7.061
0.1%

total_cholesterol_mg_dL
Real number (ℝ)

High correlation  Missing 

Distinct331
Distinct (%)46.7%
Missing59
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean4.1249083
Minimum1.12
Maximum10.48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T07:34:38.058093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.12
5-th percentile2.538
Q13.39
median4
Q34.81
95-th percentile6.016
Maximum10.48
Range9.36
Interquartile range (IQR)1.42

Descriptive statistics

Standard deviation1.1613229
Coefficient of variation (CV)0.28153907
Kurtosis3.3551237
Mean4.1249083
Median Absolute Deviation (MAD)0.7
Skewness1.0133115
Sum2924.56
Variance1.3486708
MonotonicityNot monotonic
2025-11-25T07:34:38.105128image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
410
 
1.3%
4.119
 
1.2%
3.627
 
0.9%
4.576
 
0.8%
3.896
 
0.8%
3.486
 
0.8%
4.936
 
0.8%
3.686
 
0.8%
2.775
 
0.7%
3.525
 
0.7%
Other values (321)643
83.7%
(Missing)59
 
7.7%
ValueCountFrequency (%)
1.121
0.1%
1.221
0.1%
1.291
0.1%
1.381
0.1%
1.541
0.1%
1.591
0.1%
1.82
0.3%
1.851
0.1%
2.012
0.3%
2.061
0.1%
ValueCountFrequency (%)
10.481
0.1%
10.291
0.1%
9.282
0.3%
9.041
0.1%
8.651
0.1%
7.71
0.1%
7.591
0.1%
7.31
0.1%
7.281
0.1%
6.821
0.1%

hdl_cholesterol_mg_dL
Real number (ℝ)

High correlation  Missing 

Distinct174
Distinct (%)24.5%
Missing58
Missing (%)7.6%
Infinite0
Infinite (%)0.0%
Mean1.1211127
Minimum0.28
Maximum3.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T07:34:38.150643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.28
5-th percentile0.51
Q10.83
median1.07
Q31.37
95-th percentile1.8855
Maximum3.7
Range3.42
Interquartile range (IQR)0.54

Descriptive statistics

Standard deviation0.44352229
Coefficient of variation (CV)0.39560902
Kurtosis4.4712255
Mean1.1211127
Median Absolute Deviation (MAD)0.26
Skewness1.2913394
Sum795.99
Variance0.19671202
MonotonicityNot monotonic
2025-11-25T07:34:38.198743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.813
 
1.7%
1.0413
 
1.7%
0.8513
 
1.7%
0.9313
 
1.7%
1.113
 
1.7%
1.1811
 
1.4%
0.9511
 
1.4%
110
 
1.3%
0.8410
 
1.3%
0.879
 
1.2%
Other values (164)594
77.3%
(Missing)58
 
7.6%
ValueCountFrequency (%)
0.281
0.1%
0.321
0.1%
0.332
0.3%
0.342
0.3%
0.351
0.1%
0.362
0.3%
0.372
0.3%
0.391
0.1%
0.42
0.3%
0.412
0.3%
ValueCountFrequency (%)
3.73
0.4%
2.81
 
0.1%
2.532
0.3%
2.491
 
0.1%
2.441
 
0.1%
2.311
 
0.1%
2.31
 
0.1%
2.291
 
0.1%
2.242
0.3%
2.231
 
0.1%

ldl_cholesterol_mg_dL
Real number (ℝ)

High correlation  Missing 

Distinct261
Distinct (%)36.8%
Missing58
Missing (%)7.6%
Infinite0
Infinite (%)0.0%
Mean1.6717042
Minimum0
Maximum6.04
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T07:34:38.245523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.6745
Q11.11
median1.535
Q32.07
95-th percentile3.18
Maximum6.04
Range6.04
Interquartile range (IQR)0.96

Descriptive statistics

Standard deviation0.77008108
Coefficient of variation (CV)0.4606563
Kurtosis1.8978142
Mean1.6717042
Median Absolute Deviation (MAD)0.475
Skewness1.0866871
Sum1186.91
Variance0.59302488
MonotonicityNot monotonic
2025-11-25T07:34:38.293555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.019
 
1.2%
1.129
 
1.2%
1.329
 
1.2%
1.378
 
1.0%
1.298
 
1.0%
1.187
 
0.9%
2.067
 
0.9%
1.947
 
0.9%
1.267
 
0.9%
1.767
 
0.9%
Other values (251)632
82.3%
(Missing)58
 
7.6%
ValueCountFrequency (%)
01
 
0.1%
0.331
 
0.1%
0.391
 
0.1%
0.422
 
0.3%
0.451
 
0.1%
0.461
 
0.1%
0.471
 
0.1%
0.53
0.4%
0.555
0.7%
0.564
0.5%
ValueCountFrequency (%)
6.041
0.1%
4.411
0.1%
4.281
0.1%
4.252
0.3%
4.191
0.1%
4.131
0.1%
3.971
0.1%
3.941
0.1%
3.891
0.1%
3.871
0.1%

weight_kg
Real number (ℝ)

High correlation  Missing 

Distinct360
Distinct (%)63.9%
Missing205
Missing (%)26.7%
Infinite0
Infinite (%)0.0%
Mean69.787744
Minimum35.1
Maximum140.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T07:34:38.343363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum35.1
5-th percentile47.61
Q157.9
median67.2
Q378.4
95-th percentile102.99
Maximum140.5
Range105.4
Interquartile range (IQR)20.5

Descriptive statistics

Standard deviation16.938157
Coefficient of variation (CV)0.24270962
Kurtosis1.3539238
Mean69.787744
Median Absolute Deviation (MAD)10
Skewness0.98611018
Sum39290.5
Variance286.90115
MonotonicityNot monotonic
2025-11-25T07:34:38.391756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72.35
 
0.7%
65.45
 
0.7%
59.64
 
0.5%
544
 
0.5%
53.74
 
0.5%
76.64
 
0.5%
65.64
 
0.5%
61.84
 
0.5%
55.14
 
0.5%
69.44
 
0.5%
Other values (350)521
67.8%
(Missing)205
 
26.7%
ValueCountFrequency (%)
35.11
0.1%
35.81
0.1%
36.41
0.1%
39.81
0.1%
41.61
0.1%
41.81
0.1%
421
0.1%
42.12
0.3%
42.51
0.1%
43.61
0.1%
ValueCountFrequency (%)
140.51
0.1%
135.21
0.1%
133.81
0.1%
130.61
0.1%
129.11
0.1%
121.91
0.1%
1181
0.1%
116.31
0.1%
115.81
0.1%
114.71
0.1%

height_m
Real number (ℝ)

High correlation  Missing 

Distinct194
Distinct (%)44.4%
Missing331
Missing (%)43.1%
Infinite0
Infinite (%)0.0%
Mean1.5870046
Minimum1.39
Maximum1.785
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T07:34:38.441481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.39
5-th percentile1.4918
Q11.552
median1.589
Q31.619
95-th percentile1.6772
Maximum1.785
Range0.395
Interquartile range (IQR)0.067

Descriptive statistics

Standard deviation0.057610126
Coefficient of variation (CV)0.036301172
Kurtosis1.2076171
Mean1.5870046
Median Absolute Deviation (MAD)0.034
Skewness-0.018390588
Sum693.521
Variance0.0033189266
MonotonicityNot monotonic
2025-11-25T07:34:38.493615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.5847
 
0.9%
1.5917
 
0.9%
1.5887
 
0.9%
1.617
 
0.9%
1.6066
 
0.8%
1.5686
 
0.8%
1.66
 
0.8%
1.5956
 
0.8%
1.5986
 
0.8%
1.5855
 
0.7%
Other values (184)374
48.7%
(Missing)331
43.1%
ValueCountFrequency (%)
1.391
0.1%
1.4041
0.1%
1.4051
0.1%
1.4061
0.1%
1.4161
0.1%
1.4171
0.1%
1.4571
0.1%
1.461
0.1%
1.4661
0.1%
1.4671
0.1%
ValueCountFrequency (%)
1.7851
0.1%
1.781
0.1%
1.7621
0.1%
1.7591
0.1%
1.7571
0.1%
1.7331
0.1%
1.7171
0.1%
1.7151
0.1%
1.711
0.1%
1.7081
0.1%

total_protein_extreme_flag
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.0 KiB
0.0
768 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2304
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0768
100.0%

Length

2025-11-25T07:34:38.542760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:34:38.575837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0768
100.0%

Most occurring characters

ValueCountFrequency (%)
01536
66.7%
.768
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1536
66.7%
Other Punctuation768
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01536
100.0%
Other Punctuation
ValueCountFrequency (%)
.768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2304
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01536
66.7%
.768
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01536
66.7%
.768
33.3%

HEAT_VULNERABILITY_SCORE
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.0 KiB
0.0
768 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2304
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0768
100.0%

Length

2025-11-25T07:34:38.609990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:34:38.643585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0768
100.0%

Most occurring characters

ValueCountFrequency (%)
01536
66.7%
.768
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1536
66.7%
Other Punctuation768
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01536
100.0%
Other Punctuation
ValueCountFrequency (%)
.768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2304
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01536
66.7%
.768
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01536
66.7%
.768
33.3%

HEAT_STRESS_RISK_CATEGORY
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.0 KiB
LOW
768 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2304
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLOW
2nd rowLOW
3rd rowLOW
4th rowLOW
5th rowLOW

Common Values

ValueCountFrequency (%)
LOW768
100.0%

Length

2025-11-25T07:34:38.678124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:34:38.709843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
low768
100.0%

Most occurring characters

ValueCountFrequency (%)
L768
33.3%
O768
33.3%
W768
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2304
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L768
33.3%
O768
33.3%
W768
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin2304
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
L768
33.3%
O768
33.3%
W768
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L768
33.3%
O768
33.3%
W768
33.3%

climate_daily_mean_temp
Real number (ℝ)

High correlation 

Distinct20
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.209534
Minimum8.507
Maximum21.131
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T07:34:38.741417image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum8.507
5-th percentile13.316
Q114.603
median16.425
Q317.799
95-th percentile20.357
Maximum21.131
Range12.624
Interquartile range (IQR)3.196

Descriptive statistics

Standard deviation2.3493444
Coefficient of variation (CV)0.14493596
Kurtosis0.37172559
Mean16.209534
Median Absolute Deviation (MAD)1.74
Skewness-0.23360933
Sum12448.922
Variance5.519419
MonotonicityNot monotonic
2025-11-25T07:34:38.783625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
16.42578
10.2%
17.03971
 
9.2%
17.79970
 
9.1%
15.6757
 
7.4%
14.20956
 
7.3%
14.60354
 
7.0%
13.31653
 
6.9%
14.68549
 
6.4%
20.35749
 
6.4%
17.54146
 
6.0%
Other values (10)185
24.1%
ValueCountFrequency (%)
8.5072
 
0.3%
9.61621
 
2.7%
13.31653
6.9%
13.6563
 
0.4%
14.20956
7.3%
14.5534
4.4%
14.60354
7.0%
14.68549
6.4%
14.86240
5.2%
15.6757
7.4%
ValueCountFrequency (%)
21.1311
 
0.1%
20.4656
 
0.8%
20.35749
6.4%
20.2931
 
0.1%
19.59943
5.6%
19.08434
4.4%
17.79970
9.1%
17.54146
6.0%
17.03971
9.2%
16.42578
10.2%

climate_daily_max_temp
Real number (ℝ)

High correlation 

Distinct20
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.380574
Minimum14.624
Maximum28.861
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T07:34:38.820446image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum14.624
5-th percentile18.896
Q120.108
median20.768
Q325.325
95-th percentile25.931
Maximum28.861
Range14.237
Interquartile range (IQR)5.217

Descriptive statistics

Standard deviation2.9122833
Coefficient of variation (CV)0.13012549
Kurtosis-1.5446717
Mean22.380574
Median Absolute Deviation (MAD)2.695
Skewness0.0051205477
Sum17188.281
Variance8.4813938
MonotonicityNot monotonic
2025-11-25T07:34:38.859072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
25.93178
10.2%
20.32471
 
9.2%
25.870
 
9.1%
19.12557
 
7.4%
19.27756
 
7.3%
20.58954
 
7.0%
20.76853
 
6.9%
18.89649
 
6.4%
24.31949
 
6.4%
25.00546
 
6.0%
Other values (10)185
24.1%
ValueCountFrequency (%)
14.6242
 
0.3%
17.34421
 
2.7%
18.89649
6.4%
19.12557
7.4%
19.27756
7.3%
20.10834
4.4%
20.32471
9.2%
20.58954
7.0%
20.76853
6.9%
21.4743
 
0.4%
ValueCountFrequency (%)
28.8611
 
0.1%
26.7691
 
0.1%
26.13634
4.4%
25.93178
10.2%
25.870
9.1%
25.5726
 
0.8%
25.32543
5.6%
25.00546
6.0%
24.31949
6.4%
23.46340
5.2%

climate_daily_min_temp
Real number (ℝ)

High correlation 

Distinct20
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.377281
Minimum3.333
Maximum17.507
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T07:34:38.895766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3.333
5-th percentile6.306
Q17.463
median9.869
Q312.877
95-th percentile17.507
Maximum17.507
Range14.174
Interquartile range (IQR)5.414

Descriptive statistics

Standard deviation3.3901858
Coefficient of variation (CV)0.32669307
Kurtosis-0.53633939
Mean10.377281
Median Absolute Deviation (MAD)3.008
Skewness0.26308779
Sum7969.752
Variance11.49336
MonotonicityNot monotonic
2025-11-25T07:34:38.936530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
6.30678
10.2%
14.29171
 
9.2%
10.49370
 
9.1%
12.87757
 
7.4%
8.7656
 
7.3%
9.00454
 
7.0%
6.61653
 
6.9%
11.18749
 
6.4%
17.50749
 
6.4%
9.86946
 
6.0%
Other values (10)185
24.1%
ValueCountFrequency (%)
3.33321
 
2.7%
3.9932
 
0.3%
6.0343
 
0.4%
6.30678
10.2%
6.61653
6.9%
7.46340
5.2%
8.03534
4.4%
8.7656
7.3%
9.00454
7.0%
9.86946
6.0%
ValueCountFrequency (%)
17.50749
6.4%
16.5761
 
0.1%
14.29171
9.2%
14.05743
5.6%
13.9681
 
0.1%
13.4546
 
0.8%
12.87757
7.4%
12.46534
4.4%
11.18749
6.4%
10.49370
9.1%

climate_7d_mean_temp
Real number (ℝ)

High correlation 

Distinct20
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.074818
Minimum6.912
Maximum20.253
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T07:34:38.977060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum6.912
5-th percentile12.665
Q114.198
median16.453
Q318.033
95-th percentile20.253
Maximum20.253
Range13.341
Interquartile range (IQR)3.835

Descriptive statistics

Standard deviation2.5447401
Coefficient of variation (CV)0.158306
Kurtosis0.40543248
Mean16.074818
Median Absolute Deviation (MAD)1.604
Skewness-0.46441855
Sum12345.46
Variance6.4757021
MonotonicityNot monotonic
2025-11-25T07:34:39.018938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
14.19878
10.2%
18.08171
 
9.2%
16.47170
 
9.1%
18.03357
 
7.4%
13.24356
 
7.3%
15.06454
 
7.0%
12.66553
 
6.9%
15.63349
 
6.4%
19.98249
 
6.4%
16.45346
 
6.0%
Other values (10)185
24.1%
ValueCountFrequency (%)
6.9122
 
0.3%
8.99321
 
2.7%
10.7933
 
0.4%
12.66553
6.9%
13.24356
7.3%
14.19878
10.2%
15.06454
7.0%
15.0834
4.4%
15.63349
6.4%
16.45346
6.0%
ValueCountFrequency (%)
20.25343
5.6%
19.98249
6.4%
19.8651
 
0.1%
19.6316
 
0.8%
18.7961
 
0.1%
18.08171
9.2%
18.03357
7.4%
17.69534
4.4%
16.7140
5.2%
16.47170
9.1%

climate_7d_max_temp
Real number (ℝ)

High correlation 

Distinct20
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.610668
Minimum17.442
Maximum29.909
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T07:34:39.058964image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum17.442
5-th percentile20.428
Q123.496
median25.893
Q326.761
95-th percentile27.9
Maximum29.909
Range12.467
Interquartile range (IQR)3.265

Descriptive statistics

Standard deviation2.7109412
Coefficient of variation (CV)0.11015309
Kurtosis-0.98377234
Mean24.610668
Median Absolute Deviation (MAD)2.007
Skewness-0.56315645
Sum18900.993
Variance7.3492024
MonotonicityNot monotonic
2025-11-25T07:34:39.099886image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
26.29278
10.2%
23.49671
 
9.2%
26.76170
 
9.1%
27.957
 
7.4%
20.42856
 
7.3%
21.26454
 
7.0%
20.76853
 
6.9%
23.49849
 
6.4%
25.89349
 
6.4%
27.69946
 
6.0%
Other values (10)185
24.1%
ValueCountFrequency (%)
17.4422
 
0.3%
18.69921
 
2.7%
20.42856
7.3%
20.76853
6.9%
21.26454
7.0%
21.9773
 
0.4%
23.49671
9.2%
23.49849
6.4%
23.70534
4.4%
25.89349
6.4%
ValueCountFrequency (%)
29.9091
 
0.1%
28.6961
 
0.1%
27.957
7.4%
27.69946
6.0%
27.10543
5.6%
26.76170
9.1%
26.5140
5.2%
26.29278
10.2%
26.20434
4.4%
26.0456
 
0.8%

climate_14d_mean_temp
Real number (ℝ)

High correlation 

Distinct20
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.391837
Minimum7.302
Maximum20.679
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T07:34:39.137886image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum7.302
5-th percentile12.57
Q114.8
median16.561
Q319.009
95-th percentile20.679
Maximum20.679
Range13.377
Interquartile range (IQR)4.209

Descriptive statistics

Standard deviation2.7566005
Coefficient of variation (CV)0.1681691
Kurtosis0.093105997
Mean16.391837
Median Absolute Deviation (MAD)2.184
Skewness-0.53703163
Sum12588.931
Variance7.5988464
MonotonicityNot monotonic
2025-11-25T07:34:39.180099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
14.878
10.2%
19.00971
 
9.2%
16.05770
 
9.1%
18.74557
 
7.4%
12.69956
 
7.3%
15.48354
 
7.0%
12.5753
 
6.9%
16.63749
 
6.4%
20.67949
 
6.4%
16.86346
 
6.0%
Other values (10)185
24.1%
ValueCountFrequency (%)
7.3022
 
0.3%
8.82621
 
2.7%
11.5323
 
0.4%
12.5753
6.9%
12.69956
7.3%
14.878
10.2%
15.22634
4.4%
15.48354
7.0%
16.05770
9.1%
16.56140
5.2%
ValueCountFrequency (%)
20.67949
6.4%
20.2621
 
0.1%
19.62934
4.4%
19.47543
5.6%
19.3346
 
0.8%
19.0721
 
0.1%
19.00971
9.2%
18.74557
7.4%
16.86346
6.0%
16.63749
6.4%

climate_30d_mean_temp
Real number (ℝ)

High correlation 

Distinct20
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.449453
Minimum7.313
Maximum20.948
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T07:34:39.219592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum7.313
5-th percentile12.856
Q113.947
median15.844
Q319.139
95-th percentile20.6
Maximum20.948
Range13.635
Interquartile range (IQR)5.192

Descriptive statistics

Standard deviation2.8236221
Coefficient of variation (CV)0.17165447
Kurtosis-0.51241703
Mean16.449453
Median Absolute Deviation (MAD)2.843
Skewness-0.25639706
Sum12633.18
Variance7.9728417
MonotonicityNot monotonic
2025-11-25T07:34:39.262379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
13.94778
10.2%
19.13971
 
9.2%
15.77570
 
9.1%
19.21757
 
7.4%
13.00156
 
7.3%
15.73454
 
7.0%
12.85653
 
6.9%
16.73249
 
6.4%
20.649
 
6.4%
17.57246
 
6.0%
Other values (10)185
24.1%
ValueCountFrequency (%)
7.3132
 
0.3%
9.58421
 
2.7%
11.6353
 
0.4%
12.85653
6.9%
13.00156
7.3%
13.94778
10.2%
15.20834
4.4%
15.73454
7.0%
15.77570
9.1%
15.84440
5.2%
ValueCountFrequency (%)
20.94834
4.4%
20.649
6.4%
20.2631
 
0.1%
20.1756
 
0.8%
19.4761
 
0.1%
19.21757
7.4%
19.13971
9.2%
18.96643
5.6%
17.57246
6.0%
16.73249
6.4%

climate_temp_anomaly
Real number (ℝ)

High correlation 

Distinct20
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.931276
Minimum-0.093
Maximum11.984
Zeros0
Zeros (%)0.0%
Negative57
Negative (%)7.4%
Memory size12.0 KiB
2025-11-25T07:34:39.302091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-0.093
5-th percentile-0.093
Q13.719
median6.276
Q37.913
95-th percentile11.984
Maximum11.984
Range12.077
Interquartile range (IQR)4.194

Descriptive statistics

Standard deviation3.4869388
Coefficient of variation (CV)0.58789016
Kurtosis-0.79783053
Mean5.931276
Median Absolute Deviation (MAD)1.637
Skewness-0.0032101025
Sum4555.22
Variance12.158742
MonotonicityNot monotonic
2025-11-25T07:34:39.345433image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
11.98478
10.2%
1.18571
 
9.2%
10.02570
 
9.1%
-0.09357
 
7.4%
6.27656
 
7.3%
4.85554
 
7.0%
7.91353
 
6.9%
2.16449
 
6.4%
3.71949
 
6.4%
7.43446
 
6.0%
Other values (10)185
24.1%
ValueCountFrequency (%)
-0.09357
7.4%
1.18571
9.2%
2.16449
6.4%
3.71949
6.4%
4.85554
7.0%
4.934
4.4%
5.18934
4.4%
5.3976
 
0.8%
6.27656
7.3%
6.3643
5.6%
ValueCountFrequency (%)
11.98478
10.2%
10.02570
9.1%
9.8393
 
0.4%
9.3861
 
0.1%
7.91353
6.9%
7.7621
 
2.7%
7.61940
5.2%
7.43446
6.0%
7.3112
 
0.3%
6.5051
 
0.1%

climate_standardized_anomaly
Real number (ℝ)

High correlation 

Distinct20
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0078164063
Minimum-1.401
Maximum1.604
Zeros0
Zeros (%)0.0%
Negative417
Negative (%)54.3%
Memory size12.0 KiB
2025-11-25T07:34:39.383513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-1.401
5-th percentile-1.401
Q1-0.449
median-0.069
Q30.571
95-th percentile1.09
Maximum1.604
Range3.005
Interquartile range (IQR)1.02

Descriptive statistics

Standard deviation0.75070013
Coefficient of variation (CV)96.041596
Kurtosis-0.92389359
Mean0.0078164063
Median Absolute Deviation (MAD)0.633
Skewness-0.015229641
Sum6.003
Variance0.56355069
MonotonicityNot monotonic
2025-11-25T07:34:39.428185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1.0978
10.2%
-0.70271
 
9.2%
1.07470
 
9.1%
-1.40157
 
7.4%
0.57156
 
7.3%
-0.44954
 
7.0%
0.1953
 
6.9%
-0.4149
 
6.4%
0.29849
 
6.4%
-0.44546
 
6.0%
Other values (10)185
24.1%
ValueCountFrequency (%)
-1.40157
7.4%
-0.86840
5.2%
-0.70271
9.2%
-0.622
 
0.3%
-0.44954
7.0%
-0.44546
6.0%
-0.4149
6.4%
-0.19321
 
2.7%
-0.13743
5.6%
-0.06934
4.4%
ValueCountFrequency (%)
1.6043
 
0.4%
1.0978
10.2%
1.07470
9.1%
0.9591
 
0.1%
0.71634
4.4%
0.57156
7.3%
0.4931
 
0.1%
0.29849
6.4%
0.2176
 
0.8%
0.1953
6.9%

climate_heat_day_p90
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size45.0 KiB
0.0
767 
0.571
 
1

Length

Max length5
Median length3
Mean length3.0026042
Min length3

Characters and Unicode

Total characters2306
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0767
99.9%
0.5711
 
0.1%

Length

2025-11-25T07:34:39.474584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:34:39.512879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0767
99.9%
0.5711
 
0.1%

Most occurring characters

ValueCountFrequency (%)
01535
66.6%
.768
33.3%
51
 
< 0.1%
71
 
< 0.1%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1538
66.7%
Other Punctuation768
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01535
99.8%
51
 
0.1%
71
 
0.1%
11
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2306
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01535
66.6%
.768
33.3%
51
 
< 0.1%
71
 
< 0.1%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2306
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01535
66.6%
.768
33.3%
51
 
< 0.1%
71
 
< 0.1%
11
 
< 0.1%

climate_heat_day_p95
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.0 KiB
0.0
768 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2304
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0768
100.0%

Length

2025-11-25T07:34:39.633697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:34:39.667684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0768
100.0%

Most occurring characters

ValueCountFrequency (%)
01536
66.7%
.768
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1536
66.7%
Other Punctuation768
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01536
100.0%
Other Punctuation
ValueCountFrequency (%)
.768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2304
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01536
66.7%
.768
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01536
66.7%
.768
33.3%

climate_heat_stress_index
Real number (ℝ)

High correlation 

Distinct20
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.523178
Minimum2.218
Maximum24.693
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T07:34:39.699910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.218
5-th percentile14.373
Q115.36
median16.691
Q320.175
95-th percentile21.262
Maximum24.693
Range22.475
Interquartile range (IQR)4.815

Descriptive statistics

Standard deviation2.7052804
Coefficient of variation (CV)0.15438298
Kurtosis1.0512418
Mean17.523178
Median Absolute Deviation (MAD)1.757
Skewness-0.1762757
Sum13457.801
Variance7.3185422
MonotonicityNot monotonic
2025-11-25T07:34:39.737083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
21.05778
10.2%
16.47671
 
9.2%
19.95870
 
9.1%
15.08857
 
7.4%
14.93456
 
7.3%
16.76554
 
7.0%
15.72153
 
6.9%
14.37349
 
6.4%
20.51849
 
6.4%
21.26246
 
6.0%
Other values (10)185
24.1%
ValueCountFrequency (%)
2.2182
 
0.3%
13.35121
 
2.7%
13.4283
 
0.4%
14.37349
6.4%
14.93456
7.3%
15.08857
7.4%
15.3634
4.4%
15.72153
6.9%
16.44234
4.4%
16.47671
9.2%
ValueCountFrequency (%)
24.6931
 
0.1%
22.8676
 
0.8%
22.5261
 
0.1%
21.26246
6.0%
21.05778
10.2%
20.51849
6.4%
20.17543
5.6%
19.95870
9.1%
16.76554
7.0%
16.69140
5.2%

climate_p90_threshold
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
28.409
768 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters4608
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row28.409
2nd row28.409
3rd row28.409
4th row28.409
5th row28.409

Common Values

ValueCountFrequency (%)
28.409768
100.0%

Length

2025-11-25T07:34:39.778406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:34:39.812090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
28.409768
100.0%

Most occurring characters

ValueCountFrequency (%)
2768
16.7%
8768
16.7%
.768
16.7%
4768
16.7%
0768
16.7%
9768
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3840
83.3%
Other Punctuation768
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2768
20.0%
8768
20.0%
4768
20.0%
0768
20.0%
9768
20.0%
Other Punctuation
ValueCountFrequency (%)
.768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4608
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2768
16.7%
8768
16.7%
.768
16.7%
4768
16.7%
0768
16.7%
9768
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII4608
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2768
16.7%
8768
16.7%
.768
16.7%
4768
16.7%
0768
16.7%
9768
16.7%

climate_p95_threshold
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
29.704
768 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters4608
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row29.704
2nd row29.704
3rd row29.704
4th row29.704
5th row29.704

Common Values

ValueCountFrequency (%)
29.704768
100.0%

Length

2025-11-25T07:34:39.849048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:34:39.883327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
29.704768
100.0%

Most occurring characters

ValueCountFrequency (%)
2768
16.7%
9768
16.7%
.768
16.7%
7768
16.7%
0768
16.7%
4768
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3840
83.3%
Other Punctuation768
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2768
20.0%
9768
20.0%
7768
20.0%
0768
20.0%
4768
20.0%
Other Punctuation
ValueCountFrequency (%)
.768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4608
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2768
16.7%
9768
16.7%
.768
16.7%
7768
16.7%
0768
16.7%
4768
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII4608
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2768
16.7%
9768
16.7%
.768
16.7%
7768
16.7%
0768
16.7%
4768
16.7%

climate_p99_threshold
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
31.797
768 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters4608
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row31.797
2nd row31.797
3rd row31.797
4th row31.797
5th row31.797

Common Values

ValueCountFrequency (%)
31.797768
100.0%

Length

2025-11-25T07:34:39.919557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:34:39.954542image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
31.797768
100.0%

Most occurring characters

ValueCountFrequency (%)
71536
33.3%
3768
16.7%
1768
16.7%
.768
16.7%
9768
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3840
83.3%
Other Punctuation768
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
71536
40.0%
3768
20.0%
1768
20.0%
9768
20.0%
Other Punctuation
ValueCountFrequency (%)
.768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4608
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
71536
33.3%
3768
16.7%
1768
16.7%
.768
16.7%
9768
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII4608
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
71536
33.3%
3768
16.7%
1768
16.7%
.768
16.7%
9768
16.7%

climate_season
Categorical

High correlation 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
Autumn
445 
Spring
120 
Winter
104 
Summer
99 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters4608
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSummer
2nd rowAutumn
3rd rowSummer
4th rowAutumn
5th rowAutumn

Common Values

ValueCountFrequency (%)
Autumn445
57.9%
Spring120
 
15.6%
Winter104
 
13.5%
Summer99
 
12.9%

Length

2025-11-25T07:34:39.991233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:34:40.031256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
autumn445
57.9%
spring120
 
15.6%
winter104
 
13.5%
summer99
 
12.9%

Most occurring characters

ValueCountFrequency (%)
u989
21.5%
n669
14.5%
m643
14.0%
t549
11.9%
A445
9.7%
r323
 
7.0%
i224
 
4.9%
S219
 
4.8%
e203
 
4.4%
p120
 
2.6%
Other values (2)224
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3840
83.3%
Uppercase Letter768
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u989
25.8%
n669
17.4%
m643
16.7%
t549
14.3%
r323
 
8.4%
i224
 
5.8%
e203
 
5.3%
p120
 
3.1%
g120
 
3.1%
Uppercase Letter
ValueCountFrequency (%)
A445
57.9%
S219
28.5%
W104
 
13.5%

Most occurring scripts

ValueCountFrequency (%)
Latin4608
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
u989
21.5%
n669
14.5%
m643
14.0%
t549
11.9%
A445
9.7%
r323
 
7.0%
i224
 
4.9%
S219
 
4.8%
e203
 
4.4%
p120
 
2.6%
Other values (2)224
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII4608
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
u989
21.5%
n669
14.5%
m643
14.0%
t549
11.9%
A445
9.7%
r323
 
7.0%
i224
 
4.9%
S219
 
4.8%
e203
 
4.4%
p120
 
2.6%
Other values (2)224
 
4.9%

Interactions

2025-11-25T07:34:35.248908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:19.142198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:20.020998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:20.762098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:21.499919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:22.298044image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:22.989139image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:23.695662image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:24.446618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:25.237453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:26.010782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:26.734259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:27.520691image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:28.257432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:29.003146image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:29.852024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:30.608026image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:31.346979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:32.158410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:32.894951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:33.668798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:34.493284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:35.280551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:19.177370image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:20.054760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:20.793394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:21.530789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:22.328629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:23.018431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:23.728114image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:24.478341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:25.269497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:26.044013image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:26.763388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:27.552638image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:28.288901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:29.036642image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:29.885350image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:30.640119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:31.461113image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:32.191972image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:32.926035image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:33.700409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:34.526912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:35.313370image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:19.233755image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:20.087487image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:20.826984image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:21.565489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:22.358712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:23.051320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:23.763237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:24.511744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:25.303777image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:26.077808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:26.794570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:27.588144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:28.325375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:29.070955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:29.920519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:30.674925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:31.494194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:32.225796image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:32.961402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:33.736923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:34.562809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:35.348536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:19.284507image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:20.122095image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:20.860987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:21.598896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:22.392265image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:23.083055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:23.798127image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:24.546217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:25.338718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:26.114113image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:26.827536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:27.623285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:28.358929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:29.191116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:29.957336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:30.707445image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:31.527610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:32.260717image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:32.994104image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:33.852376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:34.596697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:35.380807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:19.329799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:20.153961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:20.893677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:21.632729image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:22.423993image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:23.112977image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:23.833027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:24.662898image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:25.372909image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:26.147463image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:26.860567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:27.654648image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:28.393127image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:29.222988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:29.991865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:30.741264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:31.560192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:32.292839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:33.027651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:33.887209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:34.631928image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:35.410807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:19.361561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:20.182783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:20.924631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:21.662454image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:22.452609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:23.141267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:23.864863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:24.690736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:25.405904image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:26.177285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:26.889303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:27.686586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:28.423443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:29.256598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:30.024182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:30.772607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:31.591118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:32.322779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:33.057856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:33.917409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:34.661326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:35.443881image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:19.406645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:20.214126image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:20.954787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:21.693515image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:22.480741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:23.170119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:23.893987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:24.721443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:25.436767image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:26.208937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:26.918609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:27.716799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:28.453908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:29.287661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:30.056271image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:30.802883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:31.621152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:32.354621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:33.089391image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:33.946582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:34.691074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:35.477988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:19.442831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:20.247200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:20.988654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:21.726411image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:22.513567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:23.200563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:23.929487image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:24.753451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:25.471815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:26.241676image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:27.031032image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:27.748580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:28.487851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:29.322674image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:30.090942image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:30.837184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:31.654683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:32.388489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:33.122273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:33.981315image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:34.727223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:35.510263image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:19.472466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:20.281164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:21.020348image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:21.757725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:22.542001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:23.231430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:23.962834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2025-11-25T07:34:26.568064image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:27.353977image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:28.082323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:28.829170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:29.671498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:30.436867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:31.176325image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:31.990446image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:32.725526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:33.496794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:34.324717image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:35.076128image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:35.852592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:19.805739image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:20.625422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:21.360359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:22.167663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:22.860895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:23.562764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:24.305298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:25.105123image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:25.853023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:26.601327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:27.387733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:28.117112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:28.864295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:29.707925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:30.471294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:31.210491image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:32.023662image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:32.758939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:33.532503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:34.358650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:35.110627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:35.888487image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:19.838415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:20.659493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:21.394691image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:22.198979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:22.892500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:23.595991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:24.339908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:25.136439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:25.907179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:26.633569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:27.419828image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:28.150074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:28.899016image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:29.743803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:30.505621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:31.243867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:32.058368image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:32.793409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:33.565459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:34.392548image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:35.144892image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:35.922022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:19.870204image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:20.693388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:21.430505image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:22.232707image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:22.926020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:23.627216image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:24.375217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:25.171143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:25.941549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:26.668744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:27.450425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:28.186170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:28.933901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:29.781706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:30.541017image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:31.279361image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:32.091658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:32.826774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:33.600811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:34.425925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:35.181143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:35.956646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:19.989279image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:20.728912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:21.467594image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:22.266562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:22.958118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:23.659907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:24.411760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:25.205606image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:25.977451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:26.703367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:27.484261image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:28.219382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:28.970566image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:29.818470image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:30.575937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:31.313932image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:32.127332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:32.863313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:33.636060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:34.460342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:35.216157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-11-25T07:34:40.071713image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Age (at enrolment)BMI (kg/m²)FASTING HDLFASTING LDLWaist circumference (cm)climate_14d_mean_tempclimate_30d_mean_tempclimate_7d_max_tempclimate_7d_mean_tempclimate_daily_max_tempclimate_daily_mean_tempclimate_daily_min_tempclimate_heat_day_p90climate_heat_stress_indexclimate_seasonclimate_standardized_anomalyclimate_temp_anomalyfasting_glucose_mmol_Lhdl_cholesterol_mg_dLheight_mldl_cholesterol_mg_dLmonthtotal_cholesterol_mg_dLweight_kgyear
Age (at enrolment)1.0000.2290.0120.1570.285-0.038-0.0240.031-0.0320.0510.017-0.0250.0730.0110.0140.0920.0720.1540.012-0.0370.1570.0080.1550.2180.091
BMI (kg/m²)0.2291.0000.0200.1060.8950.0280.0310.0520.0440.0360.0400.0300.0000.0410.0000.0200.0070.1160.020-0.1120.106-0.0270.0960.9370.000
FASTING HDL0.0120.0201.0000.2690.0040.1880.2040.1750.187-0.0980.0790.1891.000-0.1130.150-0.168-0.1690.0081.000-0.0210.269-0.1020.5070.0210.159
FASTING LDL0.1570.1060.2691.0000.0910.1410.0960.1140.2050.0540.1650.1721.0000.0950.092-0.025-0.0150.0320.269-0.0811.000-0.2100.5660.0930.107
Waist circumference (cm)0.2850.8950.0040.0911.0000.0550.0730.0580.0470.0250.0600.0591.0000.0310.0440.038-0.0330.1740.0040.0470.091-0.0420.1110.8960.016
climate_14d_mean_temp-0.0380.0280.1880.1410.0551.0000.9770.4830.9480.1520.7970.9000.0000.2270.768-0.416-0.568-0.0070.188-0.0690.141-0.4940.1380.0330.569
climate_30d_mean_temp-0.0240.0310.2040.0960.0730.9771.0000.5110.9110.0940.7390.8790.0000.1460.656-0.472-0.623-0.0030.204-0.0720.096-0.4450.1550.0440.484
climate_7d_max_temp0.0310.0520.1750.1140.0580.4830.5111.0000.5440.4290.6230.2710.6990.4870.634-0.0930.094-0.0930.175-0.0920.114-0.0460.1630.0440.611
climate_7d_mean_temp-0.0320.0440.1870.2050.0470.9480.9110.5441.0000.1540.7940.8810.0000.2650.868-0.394-0.497-0.0060.187-0.0800.205-0.6040.1240.0320.554
climate_daily_max_temp0.0510.036-0.0980.0540.0250.1520.0940.4290.1541.0000.631-0.0840.9960.7620.6320.5170.649-0.049-0.098-0.0870.0540.291-0.0320.0470.541
climate_daily_mean_temp0.0170.0400.0790.1650.0600.7970.7390.6230.7940.6311.0000.6630.0850.6290.7300.093-0.023-0.0080.079-0.0690.165-0.3210.0820.0460.753
climate_daily_min_temp-0.0250.0300.1890.1720.0590.9000.8790.2710.881-0.0840.6631.0000.0910.0330.678-0.396-0.7030.0700.189-0.0450.172-0.7380.1010.0370.658
climate_heat_day_p900.0730.0001.0001.0001.0000.0000.0000.6990.0000.9960.0850.0911.0000.3410.0700.0670.9950.0001.0001.0001.0000.1251.0001.0000.000
climate_heat_stress_index0.0110.041-0.1130.0950.0310.2270.1460.4870.2650.7620.6290.0330.3411.0000.5590.3070.438-0.132-0.113-0.1100.0950.028-0.0080.0320.473
climate_season0.0140.0000.1500.0920.0440.7680.6560.6340.8680.6320.7300.6780.0700.5591.0000.6430.7540.1850.1500.0620.0920.9930.1430.1010.416
climate_standardized_anomaly0.0920.020-0.168-0.0250.038-0.416-0.472-0.093-0.3940.5170.093-0.3960.0670.3070.6431.0000.6840.206-0.1680.075-0.0250.234-0.1260.0500.707
climate_temp_anomaly0.0720.007-0.169-0.015-0.033-0.568-0.6230.094-0.4970.649-0.023-0.7030.9950.4380.7540.6841.000-0.050-0.1690.020-0.0150.572-0.093-0.0010.773
fasting_glucose_mmol_L0.1540.1160.0080.0320.174-0.007-0.003-0.093-0.006-0.049-0.0080.0700.000-0.1320.1850.206-0.0501.0000.008-0.0550.032-0.160-0.0660.1330.186
hdl_cholesterol_mg_dL0.0120.0201.0000.2690.0040.1880.2040.1750.187-0.0980.0790.1891.000-0.1130.150-0.168-0.1690.0081.000-0.0210.269-0.1020.5070.0210.159
height_m-0.037-0.112-0.021-0.0810.047-0.069-0.072-0.092-0.080-0.087-0.069-0.0451.000-0.1100.0620.0750.020-0.055-0.0211.000-0.0810.082-0.0390.1850.000
ldl_cholesterol_mg_dL0.1570.1060.2691.0000.0910.1410.0960.1140.2050.0540.1650.1721.0000.0950.092-0.025-0.0150.0320.269-0.0811.000-0.2100.5660.0930.107
month0.008-0.027-0.102-0.210-0.042-0.494-0.445-0.046-0.6040.291-0.321-0.7380.1250.0280.9930.2340.572-0.160-0.1020.082-0.2101.0000.012-0.0230.483
total_cholesterol_mg_dL0.1550.0960.5070.5660.1110.1380.1550.1630.124-0.0320.0820.1011.000-0.0080.143-0.126-0.093-0.0660.507-0.0390.5660.0121.0000.0840.098
weight_kg0.2180.9370.0210.0930.8960.0330.0440.0440.0320.0470.0460.0371.0000.0320.1010.050-0.0010.1330.0210.1850.093-0.0230.0841.0000.000
year0.0910.0000.1590.1070.0160.5690.4840.6110.5540.5410.7530.6580.0000.4730.4160.7070.7730.1860.1590.0000.1070.4830.0980.0001.000

Missing values

2025-11-25T07:34:36.109706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-25T07:34:36.296041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-25T07:34:36.408245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

study_sourceprimary_dateyearmonthlatitudelongitudejhb_subregioncityprovincecountryAge (at enrolment)datestudy_site_locationFASTING HDLFASTING LDLBMI (kg/m²)Waist circumference (cm)fasting_glucose_mmol_Ltotal_cholesterol_mg_dLhdl_cholesterol_mg_dLldl_cholesterol_mg_dLweight_kgheight_mtotal_protein_extreme_flagHEAT_VULNERABILITY_SCOREHEAT_STRESS_RISK_CATEGORYclimate_daily_mean_tempclimate_daily_max_tempclimate_daily_min_tempclimate_7d_mean_tempclimate_7d_max_tempclimate_14d_mean_tempclimate_30d_mean_tempclimate_temp_anomalyclimate_standardized_anomalyclimate_heat_day_p90climate_heat_day_p95climate_heat_stress_indexclimate_p90_thresholdclimate_p95_thresholdclimate_p99_thresholdclimate_season
217JHB_DPHRU_0132011-02-102011.02.0-26.204128.0473Central_JHBJohannesburgGautengSouth Africa19.42011-02-10Central Johannesburg (DPHRU)1.231.4124.283.05.032.771.231.4159.81.5840.00.0LOW19.59925.32514.05720.25327.10519.47518.9666.360-0.1370.00.020.17528.40929.70431.797Summer
218JHB_DPHRU_0132011-04-092011.04.0-26.204128.0473Central_JHBJohannesburgGautengSouth Africa39.42011-04-09Central Johannesburg (DPHRU)0.901.5433.6103.04.554.930.901.5483.91.5890.00.0LOW14.60320.5899.00415.06421.26415.48315.7344.855-0.4490.00.016.76528.40929.70431.797Autumn
219JHB_DPHRU_0132012-01-212012.01.0-26.204128.0473Central_JHBJohannesburgGautengSouth AfricaNaN2012-01-21Central Johannesburg (DPHRU)1.332.2033.1NaN4.765.111.332.20NaNNaN0.00.0LOW20.46525.57213.45419.63126.04519.33420.1755.3970.2170.00.022.86728.40929.70431.797Summer
220JHB_DPHRU_0132012-04-022012.04.0-26.204128.0473Central_JHBJohannesburgGautengSouth Africa40.02012-04-02Central Johannesburg (DPHRU)1.612.3733.5102.06.725.351.612.3784.71.5980.00.0LOW14.68518.89611.18715.63323.49816.63716.7322.164-0.4100.00.014.37328.40929.70431.797Autumn
221JHB_DPHRU_0132013-05-162013.05.0-26.204128.0473Central_JHBJohannesburgGautengSouth Africa42.02013-05-16Central Johannesburg (DPHRU)1.713.3630.189.05.685.891.713.3676.0NaN0.00.0LOW13.31620.7686.61612.66520.76812.57012.8567.9130.1900.00.015.72128.40929.70431.797Autumn
222JHB_DPHRU_0132011-03-192011.03.0-26.204128.0473Central_JHBJohannesburgGautengSouth Africa39.02011-03-19Central Johannesburg (DPHRU)1.163.0322.077.05.033.971.163.0368.01.7620.00.0LOW17.03920.32414.29118.08123.49619.00919.1391.185-0.7020.00.016.47628.40929.70431.797Autumn
223JHB_DPHRU_0132011-08-272011.08.0-26.204128.0473Central_JHBJohannesburgGautengSouth Africa40.02011-08-27Central Johannesburg (DPHRU)0.522.4821.5NaN4.322.520.522.48NaNNaN0.00.0LOW16.42525.9316.30614.19826.29214.80013.94711.9841.0900.00.021.05728.40929.70431.797Winter
224JHB_DPHRU_0132012-02-092012.02.0-26.204128.0473Central_JHBJohannesburgGautengSouth Africa40.02012-02-09Central Johannesburg (DPHRU)0.952.7121.277.05.484.170.952.7165.01.7590.00.0LOW20.35724.31917.50719.98225.89320.67920.6003.7190.2980.00.020.51828.40929.70431.797Summer
225JHB_DPHRU_0132013-05-092013.05.0-26.204128.0473Central_JHBJohannesburgGautengSouth Africa41.02013-05-09Central Johannesburg (DPHRU)1.042.6021.677.05.264.471.042.6066.1NaN0.00.0LOW13.31620.7686.61612.66520.76812.57012.8567.9130.1900.00.015.72128.40929.70431.797Autumn
226JHB_DPHRU_0132011-03-172011.03.0-26.204128.0473Central_JHBJohannesburgGautengSouth Africa22.22011-03-17Central Johannesburg (DPHRU)0.902.1719.369.04.253.130.902.1751.81.6460.00.0LOW17.03920.32414.29118.08123.49619.00919.1391.185-0.7020.00.016.47628.40929.70431.797Autumn
study_sourceprimary_dateyearmonthlatitudelongitudejhb_subregioncityprovincecountryAge (at enrolment)datestudy_site_locationFASTING HDLFASTING LDLBMI (kg/m²)Waist circumference (cm)fasting_glucose_mmol_Ltotal_cholesterol_mg_dLhdl_cholesterol_mg_dLldl_cholesterol_mg_dLweight_kgheight_mtotal_protein_extreme_flagHEAT_VULNERABILITY_SCOREHEAT_STRESS_RISK_CATEGORYclimate_daily_mean_tempclimate_daily_max_tempclimate_daily_min_tempclimate_7d_mean_tempclimate_7d_max_tempclimate_14d_mean_tempclimate_30d_mean_tempclimate_temp_anomalyclimate_standardized_anomalyclimate_heat_day_p90climate_heat_day_p95climate_heat_stress_indexclimate_p90_thresholdclimate_p95_thresholdclimate_p99_thresholdclimate_season
975JHB_DPHRU_0132011-06-112011.06.0-26.204128.0473Central_JHBJohannesburgGautengSouth Africa25.92011-06-11Central Johannesburg (DPHRU)1.141.5721.568.04.764.961.141.5750.41.5380.00.0LOW9.61617.3443.3338.99318.6998.8269.5847.760-0.1930.00.013.35128.40929.70431.797Winter
976JHB_DPHRU_0132012-01-212012.01.0-26.204128.0473Central_JHBJohannesburgGautengSouth AfricaNaN2012-01-21Central Johannesburg (DPHRU)1.322.1922.6NaN4.976.241.322.19NaNNaN0.00.0LOW20.46525.57213.45419.63126.04519.33420.1755.3970.2170.00.022.86728.40929.70431.797Summer
977JHB_DPHRU_0132012-05-122012.05.0-26.204128.0473Central_JHBJohannesburgGautengSouth Africa27.02012-05-12Central Johannesburg (DPHRU)1.962.6823.672.0NaN6.821.962.6854.61.5250.00.0LOW14.55020.1088.03515.08023.70515.22615.2084.9000.7160.00.016.44228.40929.70431.797Autumn
978JHB_DPHRU_0132011-06-112011.06.0-26.204128.0473Central_JHBJohannesburgGautengSouth Africa33.32011-06-11Central Johannesburg (DPHRU)1.101.0532.497.05.383.701.101.0577.81.5540.00.0LOW9.61617.3443.3338.99318.6998.8269.5847.760-0.1930.00.013.35128.40929.70431.797Winter
979JHB_DPHRU_0132011-11-162011.011.0-26.204128.0473Central_JHBJohannesburgGautengSouth Africa34.02011-11-16Central Johannesburg (DPHRU)1.401.7234.4NaN5.505.321.401.72NaNNaN0.00.0LOW19.08426.13612.46517.69526.20419.62920.9485.189-0.0690.00.015.36028.40929.70431.797Spring
980JHB_DPHRU_0132012-05-022012.05.0-26.204128.0473Central_JHBJohannesburgGautengSouth Africa34.02012-05-02Central Johannesburg (DPHRU)1.761.3237.3115.55.994.111.761.3290.81.5620.00.0LOW14.55020.1088.03515.08023.70515.22615.2084.9000.7160.00.016.44228.40929.70431.797Autumn
981JHB_DPHRU_0132013-05-082013.05.0-26.204128.0473Central_JHBJohannesburgGautengSouth Africa35.02013-05-08Central Johannesburg (DPHRU)0.421.3537.9103.06.112.350.421.3591.1NaN0.00.0LOW13.31620.7686.61612.66520.76812.57012.8567.9130.1900.00.015.72128.40929.70431.797Autumn
982JHB_DPHRU_0132011-06-072011.06.0-26.204128.0473Central_JHBJohannesburgGautengSouth Africa31.32011-06-07Central Johannesburg (DPHRU)0.911.0031.8101.05.213.520.911.0084.61.6300.00.0LOW9.61617.3443.3338.99318.6998.8269.5847.760-0.1930.00.013.35128.40929.70431.797Winter
983JHB_DPHRU_0132011-11-102011.011.0-26.204128.0473Central_JHBJohannesburgGautengSouth Africa32.02011-11-10Central Johannesburg (DPHRU)1.020.5931.2NaN4.672.931.020.59NaNNaN0.00.0LOW19.08426.13612.46517.69526.20419.62920.9485.189-0.0690.00.015.36028.40929.70431.797Spring
984JHB_DPHRU_0132012-05-022012.05.0-26.204128.0473Central_JHBJohannesburgGautengSouth Africa32.02012-05-02Central Johannesburg (DPHRU)NaNNaN33.2104.05.76NaNNaNNaN87.21.6270.00.0LOW14.55020.1088.03515.08023.70515.22615.2084.9000.7160.00.016.44228.40929.70431.797Autumn